Fires are among the most significant disturbances decreasing forest biomass (Frolking et al. 2009, Pugh et al. 2019). They may cause severe ecosystem degradation and result in loss of human life, economic devastation, social disruption, and environmental deterioration (Stephenson et al. 2012). Occurrence of forest fires is also expected to increase due to the changing climate (Szpakowski & Jensen 2019, Venäläinen et al. 2020), which can, in turn, accelerate global warming through increased release of CO2 and decreased vegetation binding CO2 (Flannigan et al. 2000, Oswald 2007).
A variety of remote sensing technologies have been used for mapping and monitoring spatial, temporal, and radiometric dimensions of forest fires (Banskota et al. 2014). Optical satellite observations (e.g. Landsat, MODIS) have widely been applied for estimating fire impacts (i.e., burned area) (Lentile et al. 2006, Chuvieco et al. 2019, 2020). However, there is a need for approaches quantifying burned biomass in a comprehensive manner (Bolton et al. 2017).
Laser scanning offers an additional dimension for optical satellite missions as it generates 3D information on trees and forests. Terrestrial laser scanning (TLS) especially provides details of tree stems (Liang et al. 2014), crowns (Seidel et al. 2011, Metz. et al. 2013), branches (Pyörälä et al. 2018), as well as biomass (Calders et al. 2015). As multitemporal TLS datasets are becoming more available, it becomes possible to monitor changes in trees (Luoma et al. 2019, 2021) and forests (Yrttimaa et al. 2020).
The aim of the study is to quantify burnt forest biomass of a controlled burning carried out in boreal forests. In other words, we will develop a methodology to quantify spectral response of burned biomass from optical satellite imagery of Sentinel-2 with terrestrial laser scanning.
We investigated a study site from the Nuuksio national park in southern Finland. The size of the study site was 1.7 ha and controlled burning was carried out on June 8th, 2021. In controlled bunnings carried out in Finland, only the vegetation on the forest floor is burned. In other words, the fire is not allowed to spread to trees, but it burns grasses, twigs, and possible fuel load on the forest floor (e.g. cleared/cut suppressed trees).
A plot of 1 ha was established within the study site and TLS data were acquired twice in the summer of 2021, between June 4 and 6 as well as between June 28 and 30 (i.e. before and after the burn). The scan locations were placed in every 10 meters including altogether 100 scans. We used RIEGL VZ400i scanner that uses time of flight measurement principal and records multiple returns from each sent laser pulse. We used scan resolution of 40 mdeg (i.e. beam divergence 0.7 mrad) resulting with scan resolution of 14 mm at 20-m distance, and laser pulse repetition rate of 1.2 MHz. The scans were filtered and registered as one harmonized point cloud with RiSCAN PRO software.
The Sentinel-2 Level-2A product was used here as it includes scene classification and atmospheric corrections. Sentinel-2 Level-2A imagery from May 24 to July 3, 2021 were utilized and gradient of spectral response (i.e., normalized burn ratio, NBR) was generated for each date. NBR uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths, and it was designed to take advantage of the different responses that disturbed and undisturbed areas will have in the NIR and SWIR spectral regions (Cohen and Goward, 2004). The NBR has showed to be related to structural component of vegetation (Epting & Verbyla 2005, Pickell et al. 2016) thus, it was utilized here as a measure for burned forest biomass of the controlled burning site.
The points of the before and after fire TLS data sets were classified into ground points and non-ground points (i.e. vegetation points) by utilizing the lasground-tool in LAStools (rapidlasso GmbH) software, and before and after digital terrain models (DTMs) were generated from a triangulation irregular network. Parameters for normalization in lasground-tool were tuned according to Ritter et al. (2017).
The difference between the before and after DTMs were studied to quantify the burned biomass on the forest floor. That was linked to the change in NBR values before and after the controlled burning. The NBR value before controlled fire was ~0.44 and it declined to 0.23 after the burn. And as we know the in the controlled burning only vegetation on the forest floor, the preliminary results indicate that also this kind of lower-level burn severity can be identified from a within-year optical satellite time series. This already brings new knowledge as previously mainly stand-replacing fires have been identified from yearly Landsat time series (White et al. 2017).
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